INHERITANCE

Did Mendel alter his results for publication?

mendel

Gregor Mendel, an austrian monk in his time (1824-1884), is regarded as the father of genetics. He paved the way of genetics for modern day.

In the following link there is a clear and helpful animation explaining in detail Mendel’s experiment on pea plants to research inheritance:

http://www.sumanasinc.com/webcontent/animations/content/mendel/mendel.html

Mendel was able to achieve the following results:

Parental plants

Hybrid plants Offspring from self-pollinating the hybrids

Ratio

Tall stem x dwarf stem

All tall

787 tall : 277 dwarf

2.84 : 1

Round seed x wrinkled seed

All round 5474 round : 1850 wrinkled 2.96 : 1
Yellow cotyledons x green cotyledons All yellow

6022 yellow : 2001 green

3.01 : 1

Purple flowers x white flowers

All purple 705 purple : 224 white

3.15 : 1

Full pods x constricted pods

All full 882 full : 299 constricted

2.95 : 1

Green unripe pods x yellow unripe pods

All green 428 green : 153 yellow 2.82 : 1
Flowers along stem x flowers at stem tip All along stem

651 along stem : 207 tip

3.14 : 1

table2

From this he was able to determine 3 very useful laws that still apply to the information we have on genetics today:

Law Definition
Law of Segregation During gamete formation, the alleles for each gene segregate from each other so that each gamete carries only one allele for each gene.
Law of Independent Assortment Genes for different traits can segregate independently during the formation of gametes.
Law of Dominance Some alleles are dominant while others are recessive; an organism with at least one dominant allele will display the effect of the dominant allele.

“In 1936, the English statistician R.A. Fisher published an analysis of Mendel’s data. His conclusion was that ‘the data of most, if not all, of the experiments have been falsified so as to agree closely with Mendel’s expectations.’ Doubts still persist about Mendel’s data -a recent estimate put the chance of getting seven ratios as close to 3:1 as Mendel’s at 1 in 33,000.”


To get ratios as close to 3:1 as Mendel’s would have required a “miracle of chance”.
What are the possible explanations apart from a “miracle of chance”?

Mendel counted 705 purple-flowered plants and 224 white-flowered plants, he then realized that the ratio of 705:224 is almost equivalent to a 3:1 ratio, probably inspiring to achieve this result in all his other tests. Another explanation apart from “a miracle of a chance” to get Mendel’s perfect results could be that he used the only the accurate results of his tests or at least the ones that were nearer to the 3:1 ratio. Lastly, Mendel could have possibly modified the data in order to make the perfect results for his experiment.

pea experiment


Many distinguished scientists, including Louis Pasteur, are known to have discarded results when they did not fit a theory. 
Is it acceptable to do this?

shredder

It is not acceptable to discard results when they do not fit the theory since it affects the legitimacy and reliability of the experiment.

In our IB Biology class sometimes when creating a lab, our  hypothesis is not correct and the results permit us to see in how and why it is wrong. If the results are discarded to achieve perfect results that prove the hypothesis, then there are many consequences to our advancement in knowledge as we will believe something that is not true. Nevertheless, sometimes results are not well corrects due to problems with the calculations of the results. Still, this does not mean  it is acceptable to discard the results, they should still be included with a justification as to why it is incorrect and the right answer accompanying it.

The scientific community is not striving for false perfection, instead answers are necessary even if they are “wrong”, they can inspire other scientists to solve the problem or it can help the scientist see where she or he went wrong. It is important that no theories are wrongly accepted.

How can we distinguish between results that are due to an error and result that falsify a theory?

oops falsification

Results that are due to an error can be identified with our knowledge if the result doesn’t make sense with the information we already know or if the results are not coherent between them (anomalous data).

On the other hand, results that falsify a theory can be found out when data is  accurate on every trial, showing that the results have been tampered with. 

That is why one should always repeat the experiment multiple times to find out if or demonstrate that the results are accurately portrayed. Also, comparing statistics and formulas in order to distinguish errors from falsifying data is essential to differentiate them both.

What standard do you use as a student in rejecting anomalous data?

anomalous dat

I do not believe any student should reject anomalous data as it occurred for a reason and it will help us determine the mistakes made in the procedure or it there are any unknown experimental/biological errors. It is vital to analyse why there is anomalous data present and include it in the final results while trying to prove the theory, explaining why such data is presented and whether it disproves a hypothesis. Everyone grows from mistakes and this data helps scientists learn from theirs and expand their minds.

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